An Accelerated PSO Based Self-organizing RBF Neural Network for Nonlinear System Identification and Modeling - Details

Author：

Ahmad, Zohaib (Ahmad, Zohaib.) | Yang, Cuili (Yang, Cuili.) | Qiao, Junfei (Qiao, Junfei.) (Scholars：乔俊飞)

Indexed by：

EI Scopus

Abstract：

In　this　paper,　an　accelerated　particle　swarm　optimization　(APSO)　based　radial　basis　function　neural　network　(RBFNN)　is　designed　for　nonlinear　system　modeling.　In　APSO-RBFNN,　the　center,　width　of　hidden　neurons,　weights　of　output　layer　and　network　size　are　optimized　by　using　the　APSO　method.　Two　nonlinear　system　modeling　experiments　are　used　to　illustrate　the　effectiveness　of　the　proposed　method.　The　simulation　results　show　that　the　proposed　method　has　obtained　good　performance　in　terms　of　network　size　and　estimation　accuracy.　©　2019,　ICST　Institute　for　Computer　Sciences,　Social　Informatics　and　Telecommunications　Engineering.

Keyword：

Functions Network layers Machine learning Nonlinear systems Particle swarm optimization (PSO) Radial basis function networks Particle accelerators

Author Community：

[ 1 ] [Ahmad, Zohaib]Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Yang, Cuili]Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Qiao, Junfei]Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China

Reprint Author's Address：

[ahmad, zohaib]faculty of information technology, beijing key laboratory of computational intelligence and intelligent system, beijing university of technology, beijing; 100124, china

Email：

ahmedzohaib03@gmail.com

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